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Aktuellste Veröffentlichungen

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    Multidimensional strategy enables scalable metabolome diversity in microbial fermentations
    (2026-01-29) Lindig, Anton; Fataeri, Makram; Hubmann, Georg; Lütz, Stephan
    Natural products (NPs) are critical sources of new drug leads, but their biosynthesis is highly sensitive to bioprocess parameters and environmental conditions, making NP discovery especially susceptible to challenges in achieving robust scalability and reproducibility of the metabolome across various cultivation systems. Here, we identified key factors that improved the metabolic footprint overlap of Streptomyces griseochromogenes across three cultivation systems, i.e. baffled shake flasks (BSF), 48 flower plates (48 FP), and a stirred tank bioreactor (STR), by 50 %. Using the classical scale-up criterion of constant oxygen availability, the metabolic footprints were found to differ considerably, with only an 18 % overlap in mass feature (MF) number. Efforts to improve overlap by varying ethanol concentration in culture medium and oxygen availability resulted in an 18 % increase. Factor analysis of 80 cultivations including variations in bioprocess conditions, growth, and MF detection identified morphology and total MF number, mainly influenced by the cultivation systems, as key factors in metabolic footprint reproducibility. Molecular network analysis revealed that 48 FP and STR share the highest number of molecular clusters. Our findings reveal that only multidimensional optimization unlocks robust metabolome scalability and reproducibility across cultivation systems, paving the way for the discovery of novel NPs.
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    Light‐activatable ubiquitin for studying linkage‐specific ubiquitin chain formation kinetics
    (2024-12-24) Banerjee, Sudakshina; Cakil, Zeyneb Vildan; Gallant, Kai; Boom, Johannes van den; Palei, Shubhendu; Meyer, Hemmo; Gersch, Malte; Summerer, Daniel
    Ubiquitination is a dynamic post-translational modification governing protein abundance, function, and localization in eukaryotes. The Ubiquitin protein is conjugated to lysine residues of target proteins, but can also repeatedly be ubiquitinated itself, giving rise to a complex code of ubiquitin chains with different linkage types. To enable studying the cellular dynamics of linkage-specific ubiquitination, light-activatable polyubiquitin chain formation is reported here. By incorporating a photocaged lysine at specific sites within ubiquitin through amber codon suppression, light-dependent activation of ubiquitin chain extension is enabled for the monitoring of linkage-specific polyubiquitination. The studies reveal rapid, minute-scale ubiquitination kinetics for K11, K48, and K63 linkages. The role of individual components of the ubiquitin-proteasome system in K48-initiated chain synthesis is further studied by small molecule inhibition. The approach expands current perturbation strategies with the ability to control linkage-specific ubiquitination with high temporal resolution and should find broad application for studying ubiquitinome dynamics.
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    Combining linguistic, behavioral and visuospatial measures to characterize multidomain impairment in dementia
    (2026-05-11) Delucchi Danhier, Renate; Mertins, Barbara
    Background/Objectives: Visuospatial impairments are among the earliest cognitive symptoms in Alzheimer’s disease (AD) and related dementias (ADRD), yet standard assessments often lack ecological validity and focus on isolated domains. This study examines whether integrating linguistic, behavioral, and eye-tracking measures provides a more comprehensive characterization of cognitive deficits within a multimodal, exploratory framework. Methods: Twenty older adults (10 with mild to moderate dementia, including AD/ADRD, and 10 age-matched controls) completed three tasks: (1) oral narrative production, (2) visuospatial behavioral tasks (manipulation, recognition, reproduction), and (3) free-viewing eye-tracking. Linguistic, behavioral (time, errors), and fixation-based measures were analyzed using non-parametric statistics, with emphasis on effect sizes and cross-domain patterns. Results: The clinical group differed consistently from controls across domains. Linguistic measures showed increased output but reduced quality, including lower syntactic complexity, more grammatical errors, greater pragmatic deviations, and reduced gist comprehension. Behavioral tasks revealed slower performance and more frequent failures. Eye-tracking differences were less pronounced, showing a tendency toward longer fixations and less efficient visual exploration. A composite multimodal index showed clear separation between groups, indicating a consistent pattern of impairment across measures. Conclusions: Cognitive differences in dementia are expressed across multiple domains, with the strongest effects in linguistic and behavioral measures. These findings highlight the value of multimodal profiles for capturing multidimensional impairment. Results should be interpreted as exploratory and require confirmation in larger, confirmatory studies.
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    Neural networks for control: design and analysis
    (2026) Teichrib, Dieter; Schulze Darup, Moritz; Fabiani, Filippo
    The universal approximation property of neural networks (NN) makes them a popular tool for approximating various functions. Therefore, it is no surprise that they also found their way in control. In control applications, NNs are commonly used to approximate complex control laws or optimal value functions (OVF) and Q-functions, which allows for simplifying optimal control problems. However, the flexibility of NNs comes with a large number of hyperparameters associated with the topology and training of the NN that need to be chosen carefully. Moreover, typical quality measures used during the training of NNs, like mean squared error, are not sufficient to ensure a safe operation of NN-based controllers. Consequently, the use of NNs in control requires the development of tailored methods that address two main challenges. The first is how to choose a suitable NN topology that allows for efficiently approximating control laws or other functions of interest in control, e.g., PWQ OVFs. The second challenge is to ensure that systems controlled by NNs can be operated safely in terms of constraint satisfaction, stability, and privacy. These requirements are unique to control applications and thus typically not considered in classical NN applications, like image or language processing. Therefore, we address these problems by developing tailored methods that enable us to derive topologies for representing continuous PWQ functions, which play a crucial role in MPC. Furthermore, by exploiting the structure of the derived topologies, we present a training method for maxout NNs that allows us to compute parameters for the NN that globally minimize the cost function considered during training. Regarding the safety of NN-based controllers, we present extensions and simplifications for analysis methods that allow us to certify stability and constraint satisfaction for linear systems based on the approximation error of the NN with respect to a stabilizing baseline controller. We also develop methods for analyzing systems controlled by NNs, based on over approximations of reachable sets, which are used to compute robustly positively invariant sets. These methods do not require a stabilizing baseline controller and can be extended for the analysis of nonlinear systems controlled by NNs. Regarding privacy, we develop a method for computing polynomial approximations that enable an efficient encrypted evaluation of NNs. In summary, the methods presented in this thesis cover a wide range of problems of NNs in control and solve or mitigate many of them. At the same time, we highlight still existing challenges, which especially include increasing the computational efficiency of the methods based on mixed-integer programming for enabling a wide use in practice.
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